Specific embodiment
Example embodiments are described in detail here, and the example is illustrated in the accompanying drawings.Following description is related to
When attached drawing, unless otherwise indicated, the same numbers in different drawings indicate the same or similar elements.Following exemplary embodiment
Described in embodiment do not represent all embodiments consistent with this specification.On the contrary, they are only and such as institute
The example of the consistent device and method of some aspects be described in detail in attached claims, this specification.
It is only to be not intended to be limiting this explanation merely for for the purpose of describing particular embodiments in the term that this specification uses
Book.The "an" of used singular, " described " and "the" are also intended to packet in this specification and in the appended claims
Most forms are included, unless the context clearly indicates other meaning.It is also understood that term "and/or" used herein is
Refer to and includes that one or more associated any or all of project listed may combine.
It will be appreciated that though various information may be described using term first, second, third, etc. in this specification, but
These information should not necessarily be limited by these terms.These terms are only used to for same type of information being distinguished from each other out.For example, not taking off
In the case where this specification range, the first information can also be referred to as the second information, and similarly, the second information can also be claimed
For the first information.Depending on context, word as used in this " if " can be construed to " ... when " or
" when ... " or " in response to determination ".
This specification provides a kind of object recommendation scheme, can construct several different homogeneity object networks and several different
Homogeneity user network, and by merging character representation of each object under different homogeneity object networks, obtain the synthesis of each object
Character representation obtains the comprehensive characteristics table of each user by merging character representation of each user under different homogeneity user networks
Show, user is then respectively adopted and the unique characteristics of object indicate, the unique characteristics of user indicate and the comprehensive characteristics table of object
Show, the comprehensive characteristics of user indicate and the unique characteristics of object indicate to come the recommendation probability for each object of user in predicting, and synthesis
This three recommendation probability, obtain combined recommendation probability.The combined recommendation Probabilistic Synthesis Heterogeneous Information of user and object, accurately
Property is higher.It is that user carries out object recommendation based on the combined recommendation probability, can effectively promotes user experience.
Above-mentioned object can include: commodity or service.For example, electric business platform can recommend the commodity sold or service to user.
Above-mentioned object may also include that information.For example, portal website can recommend information to user.
Above-mentioned object may also include that video.For example, video APP (Application, application program) can recommend to user
The videos such as TV play, film.
Certainly, in other examples, above-mentioned object can also be other entities or data, and above-mentioned object recommendation also can be applicable to
In other application scenarios, this specification is not particularly limited this.
The realization process of this specification is described below with reference to specific embodiment.
Fig. 1 and Fig. 2 are please referred to, the object recommendation method that this specification provides can comprise the following steps that
Step 102, several different homogeneity object networks are constructed, the node on behalf in the homogeneity object network is to be recommended
Object, the Lian Bian in same homogeneity object network represents connected object, and there are incidence relations under same alike result dimension.
In the present embodiment, object usually has the attribute information of various dimensions.
For example, when object is film, the dimension of the attribute information can include: director, type, watched performer
User etc..
For another example when object is commodity, the dimension of the attribute information can include: classification, material, color, pattern, purchase
The user etc. bought.
In the present embodiment, a homogeneity object network can be constructed based on the object properties information under each dimension.Institute
It states the node in homogeneity object network and represents object, Lian Bian represents connected object and there is association under corresponding attribute dimensions
Relationship.
It is commodity with object, for attribute dimensions are classification, the homogenous network of merchandise classification can be constructed, the merchandise classification net
Node on behalf commodity in network, Lian Bian represent connected commodity and belong to same category, and even the weight on side can be preset as 1 and wait ginseng
Number.
The example of Fig. 3 is please referred to, commodity 1 to commodity 4 belong to same category, such as food classification;Commodity 5 and commodity 6 belong to
In same category, such as furniture classification etc..
It is commodity with object, for attribute dimensions are the user bought, the homogenous network of goods purchase information can be constructed,
Node on behalf commodity in the goods purchase information network, Lian Bian represent connected commodity and were bought by same user, Lian Bian
Weight can based on purchase number, spend the information such as the amount of money to determine.
It is film with object, for attribute dimensions are the user watched, the homogenous network of film viewing information can be constructed,
Node on behalf film in the film viewing information network, Lian Bian represent connected film and were watched by same user, Lian Bian
Weight can based on the information such as watched time determine.
Step 104, several different homogeneity user networks are constructed, the node on behalf user in the homogeneity user network,
Lian Bian in same homogeneity user network represent the object under connected user and same alike result dimension there are incidence relation or
It represents and there is the incidence relation unrelated with object between connected user.
On the one hand, the present embodiment can construct several homogeneity users based on the incidence relation of user and each attribute dimensions object
Network.
Specifically, the incidence relation based on user and each attribute dimensions object can construct a homogeneity user network.
Node on behalf user in the homogeneity user network, Lian Bian represent connected user and deposit with the object under corresponding attribute dimensions
In incidence relation.
By taking object is film as an example, the incidence relation can include: viewing, comment, collection etc..
By taking object is commodity as an example, the incidence relation can include: purchase, browsing, comment etc..
It in one example, is constructed homogeneity user network for director when object is film, object properties dimension
In Lian Bian represent between film made by connected user and same director that there are the relationships such as viewing, comment, collection.
Assuming that there is even side in the homogeneity user network, between user A and user B, then user A and user B can be indicated
All watched the film of director's Shi Diwen 〃 Si Pierre's Burger production.For example, user A viewing is flashed back past events " No.1 player ", user B
" Jurassic Park " is flashed back past events in viewing.
There is even side between user A and user C, then can indicate that user A and user C have collected director James 〃 card plum
The film of grand production.For example, user A has collected Film Titanic, user C has collected film " abnormity " etc..
It in another example, is constructed homogeneity user network for classification when object is commodity, object properties dimension
Lian Bian in network, which is represented between connected user and identical commodity, has the relationships such as purchase, browsing, comment.
It is assumed that there is even side in the homogeneity user network, between user W and user Y, then user W and user Y can be indicated
There are the relationships such as purchase, browsing between the commodity of the same category.For example, user W and user Y buy too small household appliances yes
Commodity etc..
It is worth noting that, can also be distinguished to the type of incidence relation when constructing homogeneity user network.With object
For being film, can by viewing, comment, collect these three incidence relations and distinguish.For example, being directed to this attribute dimension of movie director
Degree, can establish 3 homogeneity user networks, and the Lian Bian in one of homogeneity user network represents connected user and watched together
The film of one director's production;Lian Bian in another homogeneity user network represents the connected excessively same director of user comment
The film of production;Lian Bian in another homogeneity user network represents the electricity that connected user collected same director's production
Shadow etc., this specification is not particularly limited this.
On the other hand, the present embodiment can also construct several homogeneities based on the incidence relation unrelated with object between user
User network.
The incidence relation unrelated with object may include social networks, treasury trade relationship, equipment using relationship etc..
For example, a homogeneity user network can be constructed according to the social networks of user.Company side in the homogeneity user network
It can represent between connected user that there are the social networks such as friend relation, concern relation.
For another example also a homogeneity user network can be constructed according to the transfer information of user.In the homogeneity user network
Lian Bianke, which is represented, has relationship etc. of transferring accounts between connected user.
For another example also a homogeneity user network can be constructed using relationship according to the equipment between user.Homogeneity user
Lian Bianke in network is represented and was used identical equipment between connected user.
In the present specification, can be used the technical solution recorded in abovementioned steps 102 and step 104 construct respectively it is several same
Matter object network and several homogeneity user networks.
In other examples, the heterogeneous network for carrying various information can also be first generated, heterogeneous network progress is then based on
The building of homogenous network.
For example, can be according between incidence relation, user and the user between user property, object properties, user and object
The information such as incidence relation generate heterogeneous network, the node in the heterogeneous network can represent user, object, user property, object
Attribute etc..
When the company's side connecting object and object properties of the heterogeneous network, which, which represents the object and have, is connected
Object properties.
When company's side connecting object of the heterogeneous network and user, which is represented between connected object and user
There are incidence relations.
When the company side of the heterogeneous network connects user and when user property, which, which represents the user and have, is connected
User property.
When the company side of the heterogeneous network connects user and user, which represents has between connected user
Friend, the incidence relations such as transfer accounts.
Certainly, the company side in heterogeneous network can also connect user and object properties etc., and this specification is no longer gone to live in the household of one's in-laws on getting married one by one herein
It states.
In heterogeneous network, first path can contain semantic information abundant.Wherein, first path is for connecting isomery
The relation path of two nodes in network.
For example, member path U-U (User-User) can contain the information such as social networks, relationship of transferring accounts between user.
For another example member path U-I-U (User-Item-User) can contain the common purchaser record between user.
For another example member path I-U-I (Item-User-Item) can contain the record etc. bought jointly between commodity.
In the present embodiment, the heterogeneous network is divided into several described same by the method that first path random walk can be used
Matter object network and several homogeneity user networks.
For example, to either direction migration, can judge the user on path from any node in the heterogeneous network
It whether there is above-mentioned incidence relation between user node, object and Object node, and extractable there are above-mentioned incidence relations
Node is to carry out the building of homogeneity object network or homogeneity user network.
It is worth noting that, being needed when carrying out the building of homogeneity object network by the way of first path random walk
Filter non-object node.
For example, need to filter out user node U when carrying out the building of homogeneity object network based on I-U-I, building is only wrapped
Include the homogeneity object network of Object node.
Similar, when constructing homogeneity user network, also need to filter out non-user node.
For example, need to filter out Object node I when carrying out the building of homogeneity user network based on U-I-U, building is only wrapped
Include the homogeneity user network of user node.
In the present embodiment, the heterogeneous network building for carrying various information is more quick, different using first path random walk
The building that the mode of network forming network carries out homogeneity object network and homogeneity user network can effectively improve the building efficiency of homogenous network,
Also, the homogenous network based on heterogeneous network construction remains the characteristic of heterogeneous network.
Step 106, for each object, character representation of the object under different homogeneity object networks is merged, is obtained
The comprehensive characteristics of the object indicate.
It can be every in the homogeneity object network for each homogeneity object network constructed based on abovementioned steps 102
A object generates corresponding character representation.
The form that features described above indicates can include: low-dimensional vector, matrix, distribution etc..
For example, the character representation that Skip-Gram model generates each Object node in each homogeneity object network can be used.
Certainly, in other examples, it is that the object in homogeneity object network generates character representation that other models, which can also be used,
This specification is not particularly limited this.
In the present embodiment, after generating character representation for the object in each homogeneity object network, for same right
As, for example, same portion's film, the same commodity, same video etc., can merge each character representation of the object,
The comprehensive characteristics for obtaining the object indicate.
It is assumed that 10 homogeneity object networks are built in abovementioned steps 102, then it in this step, can for each object
10 character representations corresponding to the object are merged, obtaining the comprehensive characteristics indicates.
In the present embodiment, attention mechanism can be used to merge character representation, for example, attention mechanism can be used
It first determines weight of each object in different homogeneity object networks, the comprehensive of the object is then obtained using average weighted mode
Close character representation.
In other examples, the modes such as averaging can also be used to merge character representation, this specification does not make this
It is specifically limited.
In the present embodiment, attributive character of the object under each dimension has been merged in the comprehensive characteristics expression of object, effectively
Carry the Heterogeneous Information of object.
Step 108, for each user, character representation of the user under different homogeneity user networks is merged, is obtained
The comprehensive characteristics of the user indicate.
It can be every in the homogeneity user network for each homogeneity user network constructed based on abovementioned steps 104
A user generates corresponding character representation.Then, for the same user, each character representation of the user can be melted
It closes, the comprehensive characteristics for obtaining the user indicate.
In the present embodiment, the comprehensive characteristics of user indicate to have merged the various features of user, effectively carry user's
Heterogeneous Information.
The method that generating user's comprehensive characteristics in this step indicates can refer to generation object comprehensive characteristics in abovementioned steps 106
Method used by indicating, this is no longer going to repeat them for this specification.
Step 110, it is indicated using the unique characteristics of user and the comprehensive characteristics of object is expressed as each object of user in predicting
First recommends probability.
In the present embodiment, generate user unique characteristics indicate when, can for each user generate corresponding 0/1 to
Amount, then carries out insertion processing to 0/1 vector, and the unique characteristics for obtaining the user indicate.
Above-mentioned 0/1 vector is to indicate user using a very long vector, and the dimension of the vector is number of users, the use
The element value of dimension is 1 where family, and the element value of other dimensions is all 0.
It is assumed that one shares 10,000,000 users, then 0/1 vector of each user is 10,000,000 dimensions, the 1st of user Zhang San the
The element value of a dimension is 1, other dimension element values are 0, then 0/1 vector of Zhang San is represented by [1,0,0,0,0 ...];User
The element value of the 3rd dimension of Li Si is 1, other dimension element values are 0, then 0/1 vector of Li Si be represented by [0,0,1,0,
0…]。
In the present embodiment, insertion processing (Embedding) can be carried out to 0/1 vector of each user, by the 0/1 of higher-dimension
DUAL PROBLEMS OF VECTOR MAPPING obtains the low-dimensional character representation of each user to lower dimensional space.Since the low-dimensional character representation does not include user's
The Heterogeneous Informations such as attribute, social activity, therefore the unique characteristics that the low-dimensional character representation can be known as to user indicate.
In the present embodiment, the unique characteristics that user can be used indicate to indicate prediction object recommendation with the comprehensive characteristics of object
Probability, the recommendation probability of as each each object of user in predicting.For user's unique characteristics table can will be based on convenient for subsequent differentiation
Showing indicates that the recommendation probability predicted is known as the first recommendation probability with object comprehensive characteristics, and the first recommendation probability effectively supplements
The Heterogeneous Information of object.
In the present embodiment, the first multiple perceptron model for having trained can be used to predict that described first recommends probability,
For example, by the first multiple perceptron model that the unique characteristics of user indicate and the expression input of the comprehensive characteristics of object has been trained,
It exports above-mentioned first and recommends probability.
The training process of first multiple perceptron model can include:
It is indicated using the unique characteristics of user and the comprehensive characteristics of object is indicated as the defeated of the first multiple perceptron model
Enter, and label is determined based on the historical context relationship of user and object, then first multiple perceptron model is instructed
Practice.
It wherein, can will be between the user and the object if user and object have incidence relation in history
Label is set as 1;It, can will be between the user and the object if incidence relation is not present in user and object in history
Label is set as 0 etc..
For example, Zhang San in the soy sauce for once buying certain brand, then can set 1 for the label of Zhang San and the soy sauce;Li Si
Certain clothes was not bought, then can set 0 etc. for the label of Li Si and the clothes.
Step 112, it is indicated using the comprehensive characteristics of user and the unique characteristics of object is expressed as each object of user in predicting
Second recommends probability.
In the present embodiment, similar with the expression of the unique characteristics of user, it, can also when the unique characteristics for generating object indicate
Corresponding 0/1 vector is generated for each object, insertion processing then is carried out to 0/1 vector, obtains the object itself
Character representation.
The generation that the generation of above-mentioned 0/1 vector, unique characteristics indicate can refer to object unique characteristics table in abovementioned steps 110
The generating mode shown, this is no longer going to repeat them for this specification.
In the present embodiment, the comprehensive characteristics that user can be used indicate to indicate prediction object recommendation with the unique characteristics of object
Probability, the recommendation probability of as each each object of user in predicting.For user's comprehensive characteristics table can will be based on convenient for subsequent differentiation
Showing indicates that the recommendation probability predicted is known as the second recommendation probability with object unique characteristics, and the second recommendation probability effectively supplements
The Heterogeneous Information of user.
In the present embodiment, the second multiple perceptron model for having trained can be used to predict that described second recommends probability,
For example, by the second multiple perceptron model that the comprehensive characteristics of user indicate and the expression input of the unique characteristics of object has been trained,
It exports above-mentioned second and recommends probability.
It is similar with aforementioned first multiple perceptron model, the training process of second multiple perceptron model can include:
It is indicated using the comprehensive characteristics of user and the unique characteristics of object is indicated as the defeated of the second multiple perceptron model
Enter, and label is determined based on the historical context relationship of user and object, then second multiple perceptron model is instructed
Practice.
The present embodiment step 110 and step 112 model the complex interaction between user and object using multi-layer perception (MLP), can
Preferably to model the connection between user and object, the accuracy of prediction result is improved.
Step 114, it is indicated using the unique characteristics of user and the unique characteristics of object is expressed as each object of user in predicting
Third recommends probability.
In the present embodiment, the unique characteristics that user can be used indicate to indicate prediction object recommendation with the unique characteristics of object
Probability, the recommendation probability of as each each object of user in predicting.For user's unique characteristics table can will be based on convenient for subsequent differentiation
Showing indicates that the recommendation probability predicted is known as third and recommends probability with object unique characteristics.
In the present embodiment, the unique characteristics of user can be indicated and the unique characteristics of object indicates to use as input
Matrix decomposition algorithm predicts that the third recommends probability.
For example, can indicate each user's unique characteristics to do dot product with the expression of each object unique characteristics, each use is obtained
Then real number value between family and each object can be used Sigmoid function for the real number value and be converted to probability value, and can incite somebody to action
Probability value recommends probability as the third.
As an example it is assumed that the unique characteristics of user A indicate and the unique characteristics of commodity 1 indicate to do what dot product obtained later
Real number value is handled using Sigmoid function, obtains probability value 0.75, then it represents that recommends the third that commodity 1 recommend user A
Probability is 0.75.
Certainly, in other examples, other modes can also be used and predict that the third recommends probability, this specification to this not
Make specifically limited.
Step 116, comprehensive described first recommends probability, described second that probability and the third is recommended to recommend probability, obtains
Combined recommendation probability for object recommendation.
In one example, the mode comprehensive described first that averaging can be used recommends probability, described second to recommend probability
Recommend probability with the third, obtains the combined recommendation probability.
As an example it is assumed that being 0.6 by the first recommendation probability that commodity 1 recommend user A, the second recommendation probability is 0.7,
It is 0.8 that third, which recommends probability, then can obtain recommending commodity 1 into the combined recommendation probability of user A being 0.7 after being averaging.
In another example, average weighted mode comprehensive described first, which can also be used, recommends probability, described second to push away
It recommends probability and the third recommends probability, obtain the combined recommendation probability, this specification is not particularly limited this.
In the present embodiment, the combined recommendation probability is that the foundation of object recommendation can be according to described for each user
The sequence of combined recommendation probability from high to low is ranked up each object, then can will be arranged in front several object recommendations
Give the user.For example, 5 object recommendations will be arranged in front to the user etc..
This specification can construct several different homogeneity object networks and several different same it can be seen from above description
Matter user network, and by merging character representation of each object under different homogeneity object networks, obtain the comprehensive special of each object
Sign indicate, the comprehensive characteristics indicate can effective integration object various Heterogeneous Informations.This specification also passes through each user of fusion and exists
Character representation under different homogeneity user networks, the comprehensive characteristics for obtaining each user indicate that comprehensive characteristics expression can effectively melt
Share the various Heterogeneous Informations at family.Itself spy of the unique characteristics expression of user and object, user can be respectively adopted in this specification
Sign indicates and the comprehensive characteristics of object indicate, the comprehensive characteristics of user indicate and the unique characteristics of object indicate to come for user in predicting
The recommendation probability of each object, and comprehensive this three recommendation probability, obtain combined recommendation probability.The combined recommendation Probabilistic Synthesis is used
The Heterogeneous Information at family and object, accuracy are higher.Also, the combined recommendation probability calculation based on Heterogeneous Information, can be in certain journey
Cold start-up problem is avoided on degree, can also resist the influence of network sparsity and noise to probability calculation is recommended to a certain extent.
It is that user carries out object recommendation based on the combined recommendation probability, can effectively promotes user experience.
It applies it is worth noting that, the numerical procedure for the combined recommendation probability that this specification provides is removed in object recommendation scene
Outside, various scoring scenes, such as film scoring, commodity scoring etc. are applied also for, this specification is not particularly limited this.
This specification also provides a kind of training method of object recommendation model, and end to end model (end to end) can be used
To realize.
In the present embodiment, step 102 and step 104 shown in FIG. 1 are please referred to, several different same confrontations can also be constructed
It, then can be using each homogeneity object network and each homogeneity object network as defeated as network and several different homogeneity user networks
Enter, and using the label determined by the historical context relationship of user and object, to the end to end model progress for object recommendation
Training.
The end to end model for object recommendation may include two class submodels: indicating to generate submodel and recommends submodule
Type.
Wherein, the expression generates submodel and can be used for merging the object in different homogeneity objects for each object
Character representation under network, the comprehensive characteristics for obtaining the object indicate;For each user, the user is merged different same
Character representation under matter user network, the comprehensive characteristics for obtaining the user indicate;And generate the unique characteristics expression of user
It is indicated with the unique characteristics of object.
The recommendation submodel can be used for being indicated using the unique characteristics of user and the comprehensive characteristics of object indicated as defeated
Enter, export each object first recommends probability;It is indicated using the comprehensive characteristics of user and the unique characteristics of object is as input, it is defeated
The second of each object recommends probability out;It is indicated using the unique characteristics of user and the unique characteristics of object is indicated as input, it is defeated
The third of each object recommends probability out;And the comprehensive first recommendation probability, the second recommendation probability and the third push away
Probability is recommended, the combined recommendation probability for object recommendation is obtained.
Above-mentioned expression generates submodel and the realization process of submodel is recommended to can refer to aforementioned embodiment shown in FIG. 1, this
This is no longer going to repeat them for specification.
It in the present embodiment, can integrating representation life when being trained to the above-mentioned end to end model for object recommendation
Joint training is carried out with the recommendation loss function of submodel is recommended at the expression loss function of submodel.
In other examples, if above-mentioned homogeneity object network and homogeneity user network are based on first path random walk isomery
The mode of network constructs, and when carrying out above-mentioned model training end to end, original heterogeneous network can be used instead of above-mentioned homogeneity
Object network and homogeneity user network are using as mode input.
The present embodiment using object recommendation model end to end, it is subsequent can be directly by the homogeneity object network and homogeneity of building
User network inputs the end to end model trained, and exports the combined recommendation probability between each object and each user, use is more square
Just.Also, model is more to the self-regulation space of data, and whole compatible degree is higher.
Corresponding with the embodiment of aforementioned object recommended method, this specification additionally provides the implementation of object recommendation device
Example.
The embodiment of this specification object recommendation device can be using on the server.Installation practice can pass through software
It realizes, can also be realized by way of hardware or software and hardware combining.Taking software implementation as an example, as on a logical meaning
Device, be to be read computer program instructions corresponding in nonvolatile memory by the processor of server where it
Operation is formed in memory.For hardware view, as shown in figure 4, the server where this specification object recommendation device
A kind of hardware structure diagram is implemented other than processor shown in Fig. 4, memory, network interface and nonvolatile memory
Server in example where device can also include other hardware generally according to the actual functional capability of the server, no longer superfluous to this
It states.
Fig. 5 is a kind of block diagram of object recommendation device shown in one exemplary embodiment of this specification.
Referring to FIG. 5, the object recommendation device 400 can be applied in aforementioned server shown in Fig. 4, include:
Object network construction unit 401, user network construction unit 402, object indicate that generation unit 403, user indicate generation unit
404, the first recommendation unit 405, the second recommendation unit 406, third recommendation unit 407 and combined recommendation unit 408.
Wherein, object network construction unit 401 constructs several different homogeneity object networks, the homogeneity object network
In node on behalf object to be recommended, the Lian Bian in same homogeneity object network represents connected object and ties up in same alike result
There are incidence relations under degree;
User network construction unit 402 constructs several different homogeneity user networks, the section in the homogeneity user network
Point represents user, and the object that the Lian Bian in same homogeneity user network is represented under connected user and same alike result dimension exists
There is the incidence relation unrelated with object between the user that incidence relation or representative are connected;
Object indicates that generation unit 403 merges spy of the object under different homogeneity object networks for each object
Sign indicates that the comprehensive characteristics for obtaining the object indicate;
User indicates that generation unit 404 merges spy of the user under different homogeneity user networks for each user
Sign indicates that the comprehensive characteristics for obtaining the user indicate;
First recommendation unit 405 is indicated using the unique characteristics of user and the comprehensive characteristics of object is expressed as user in predicting
The first of each object recommends probability;
Second recommendation unit 406 is indicated using the comprehensive characteristics of user and the unique characteristics of object is expressed as user in predicting
The second of each object recommends probability;
Third recommendation unit 407 is indicated using the unique characteristics of user and the unique characteristics of object is expressed as user in predicting
The third of each object recommends probability;
Combined recommendation unit 408, comprehensive described first recommends probability, described second that probability and the third is recommended to recommend generally
Rate obtains the combined recommendation probability for object recommendation.
Optionally, the building process of the homogeneity object network and the homogeneity user network includes:
According to the incidence relation between user property, object properties, user and object and the pass between user and user
Connection relationship constructs heterogeneous network, node on behalf user, object, user property or the object properties of the heterogeneous network;
The heterogeneous network is divided by several homogeneity object networks and several using first path random walk method
The homogeneity user network.
Optionally, described first recommends probability and described second that probability is recommended to predict based on multiple perceptron model.
Optionally, the training process of the multiple perceptron model includes:
It is indicated using the unique characteristics of user and the comprehensive characteristics of object indicates to be used as input feature vector, to the first Multilayer Perception
Machine model is trained, and first multiple perceptron model is for predicting that described first recommends probability;
It is indicated using the comprehensive characteristics of user and the unique characteristics of object is indicated as input feature vector to the second Multilayer Perception
Machine model is trained, and second multiple perceptron model is for predicting that described second recommends probability;
Wherein, the mark for being trained to first multiple perceptron model and second multiple perceptron model
Label are determined based on the historical context relationship of user and object.
Optionally, the third recommendation unit 407:
The unique characteristics of user are indicated and the unique characteristics of object indicate to use matrix decomposition algorithm as input to use
Predict that the third of each object recommends probability in family.
Optionally, the generating process of the unique characteristics expression includes:
Corresponding 0/1 vector is generated respectively for each user and each object;
Insertion processing is carried out to 0/1 vector, the unique characteristics for obtaining the user or the object indicate.
Optionally, the fusion process of the comprehensive characteristics expression includes:
Character representation of the object under different homogeneity object networks is merged using attention mechanism, obtains the object
Comprehensive characteristics indicate;
Character representation of the user under different homogeneity user networks is merged using attention mechanism, obtains the user
Comprehensive characteristics indicate.
Optionally, the incidence relation unrelated with object includes one or more of:
Social networks, treasury trade relationship, equipment use relationship.
The function of each unit and the realization process of effect are specifically detailed in the above method and correspond to step in above-mentioned apparatus
Realization process, details are not described herein.
For device embodiment, since it corresponds essentially to embodiment of the method, so related place is referring to method reality
Apply the part explanation of example.The apparatus embodiments described above are merely exemplary, wherein described be used as separation unit
The unit of explanation may or may not be physically separated, and component shown as a unit can be or can also be with
It is not physical unit, it can it is in one place, or may be distributed over multiple network units.It can be according to actual
The purpose for needing to select some or all of the modules therein to realize this specification scheme.Those of ordinary skill in the art are not
In the case where making the creative labor, it can understand and implement.
System, device, module or the unit that above-described embodiment illustrates can specifically realize by computer chip or entity,
Or it is realized by the product with certain function.A kind of typically to realize that equipment is computer, the concrete form of computer can
To be personal computer, laptop computer, cellular phone, camera phone, smart phone, personal digital assistant, media play
In device, navigation equipment, E-mail receiver/send equipment, game console, tablet computer, wearable device or these equipment
The combination of any several equipment.
Corresponding with the embodiment of aforementioned object recommended method, this specification also provides a kind of object recommendation device, the dress
Set includes: processor and the memory for storing machine-executable instruction.Wherein, processor and memory are usually by interior
Portion's bus is connected with each other.In other possible implementations, the equipment is also possible that external interface, with can be with other
Equipment or component are communicated.
It in the present embodiment, can by reading and executing the machine corresponding with object recommendation logic of the memory storage
It executes instruction, the processor is prompted to:
Several different homogeneity object networks are constructed, the object to be recommended of the node on behalf in the homogeneity object network,
Lian Bian in same homogeneity object network represents connected object, and there are incidence relations under same alike result dimension;
Construct several different homogeneity user networks, the node on behalf user in the homogeneity user network, same homogeneity
The object that Lian Bian in user network is represented under connected user and same alike result dimension there are incidence relation or represents company, institute
There is the incidence relation unrelated with object between the user connect;
For each object, character representation of the object under different homogeneity object networks is merged, the object is obtained
Comprehensive characteristics indicate;
For each user, character representation of the user under different homogeneity user networks is merged, the user is obtained
Comprehensive characteristics indicate;
It is indicated using the unique characteristics of user and the comprehensive characteristics of object is expressed as the first of each object of user in predicting and recommends
Probability;
It is indicated using the comprehensive characteristics of user and the unique characteristics of object is expressed as the second of each object of user in predicting and recommends
Probability;
Indicate that the third that each object of user in predicting is expressed as with the unique characteristics of object is recommended using the unique characteristics of user
Probability;
Comprehensive described first recommends probability, described second that probability and the third is recommended to recommend probability, obtains for object
The combined recommendation probability of recommendation.
Optionally, the building process of the homogeneity object network and the homogeneity user network includes:
According to the incidence relation between user property, object properties, user and object and the pass between user and user
Connection relationship constructs heterogeneous network, node on behalf user, object, user property or the object properties of the heterogeneous network;
The heterogeneous network is divided by several homogeneity object networks and several using first path random walk method
The homogeneity user network.
Optionally, described first recommends probability and described second that probability is recommended to predict based on multiple perceptron model.
Optionally, the training process of the multiple perceptron model includes:
It is indicated using the unique characteristics of user and the comprehensive characteristics of object indicates to be used as input feature vector, to the first Multilayer Perception
Machine model is trained, and first multiple perceptron model is for predicting that described first recommends probability;
It is indicated using the comprehensive characteristics of user and the unique characteristics of object is indicated as input feature vector to the second Multilayer Perception
Machine model is trained, and second multiple perceptron model is for predicting that described second recommends probability;
Wherein, the mark for being trained to first multiple perceptron model and second multiple perceptron model
Label are determined based on the historical context relationship of user and object.
Optionally, it is indicated in the unique characteristics using user and the unique characteristics of object is expressed as each object of user in predicting
When third recommends probability, the processor is prompted to:
The unique characteristics of user are indicated and the unique characteristics of object indicate to use matrix decomposition algorithm as input to use
Predict that the third of each object recommends probability in family.
Optionally, the generating process of the unique characteristics expression includes:
Corresponding 0/1 vector is generated respectively for each user and each object;
Insertion processing is carried out to 0/1 vector, the unique characteristics for obtaining the user or the object indicate.
Optionally, the fusion process of the comprehensive characteristics expression includes:
Character representation of the object under different homogeneity object networks is merged using attention mechanism, obtains the object
Comprehensive characteristics indicate;
Character representation of the user under different homogeneity user networks is merged using attention mechanism, obtains the user
Comprehensive characteristics indicate.
Optionally, the incidence relation unrelated with object includes one or more of:
Social networks, treasury trade relationship, equipment use relationship.
Corresponding with the embodiment of aforementioned object recommended method, this specification also provides a kind of computer-readable storage medium
Matter is stored with computer program on the computer readable storage medium, which performs the steps of when being executed by processor
Several different homogeneity object networks are constructed, the object to be recommended of the node on behalf in the homogeneity object network,
Lian Bian in same homogeneity object network represents connected object, and there are incidence relations under same alike result dimension;
Construct several different homogeneity user networks, the node on behalf user in the homogeneity user network, same homogeneity
The object that Lian Bian in user network is represented under connected user and same alike result dimension there are incidence relation or represents company, institute
There is the incidence relation unrelated with object between the user connect;
For each object, character representation of the object under different homogeneity object networks is merged, the object is obtained
Comprehensive characteristics indicate;
For each user, character representation of the user under different homogeneity user networks is merged, the user is obtained
Comprehensive characteristics indicate;
It is indicated using the unique characteristics of user and the comprehensive characteristics of object is expressed as the first of each object of user in predicting and recommends
Probability;
It is indicated using the comprehensive characteristics of user and the unique characteristics of object is expressed as the second of each object of user in predicting and recommends
Probability;
Indicate that the third that each object of user in predicting is expressed as with the unique characteristics of object is recommended using the unique characteristics of user
Probability;
Comprehensive described first recommends probability, described second that probability and the third is recommended to recommend probability, obtains for object
The combined recommendation probability of recommendation.
Optionally, the building process of the homogeneity object network and the homogeneity user network includes:
According to the incidence relation between user property, object properties, user and object and the pass between user and user
Connection relationship constructs heterogeneous network, node on behalf user, object, user property or the object properties of the heterogeneous network;
The heterogeneous network is divided by several homogeneity object networks and several using first path random walk method
The homogeneity user network.
Optionally, described first recommends probability and described second that probability is recommended to predict based on multiple perceptron model.
Optionally, the training process of the multiple perceptron model includes:
It is indicated using the unique characteristics of user and the comprehensive characteristics of object indicates to be used as input feature vector, to the first Multilayer Perception
Machine model is trained, and first multiple perceptron model is for predicting that described first recommends probability;
It is indicated using the comprehensive characteristics of user and the unique characteristics of object is indicated as input feature vector to the second Multilayer Perception
Machine model is trained, and second multiple perceptron model is for predicting that described second recommends probability;
Wherein, the mark for being trained to first multiple perceptron model and second multiple perceptron model
Label are determined based on the historical context relationship of user and object.
Optionally, the unique characteristics using user indicate and the unique characteristics of object are expressed as each object of user in predicting
Third recommend probability, comprising:
The unique characteristics of user are indicated and the unique characteristics of object indicate to use matrix decomposition algorithm as input to use
Predict that the third of each object recommends probability in family.
Optionally, the generating process of the unique characteristics expression includes:
Corresponding 0/1 vector is generated respectively for each user and each object;
Insertion processing is carried out to 0/1 vector, the unique characteristics for obtaining the user or the object indicate.
Optionally, the fusion process of the comprehensive characteristics expression includes:
Character representation of the object under different homogeneity object networks is merged using attention mechanism, obtains the object
Comprehensive characteristics indicate;
Character representation of the user under different homogeneity user networks is merged using attention mechanism, obtains the user
Comprehensive characteristics indicate.
Optionally, the incidence relation unrelated with object includes one or more of:
Social networks, treasury trade relationship, equipment use relationship.
It is above-mentioned that this specification specific embodiment is described.Other embodiments are in the scope of the appended claims
It is interior.In some cases, the movement recorded in detail in the claims or step can be come according to the sequence being different from embodiment
It executes and desired result still may be implemented.In addition, process depicted in the drawing not necessarily require show it is specific suitable
Sequence or consecutive order are just able to achieve desired result.In some embodiments, multitasking and parallel processing be also can
With or may be advantageous.
The foregoing is merely the preferred embodiments of this specification, all in this explanation not to limit this specification
Within the spirit and principle of book, any modification, equivalent substitution, improvement and etc. done should be included in the model of this specification protection
Within enclosing.